


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def 							UpperCamelCase_(					_A :str					,  _A :Any	)->		Any:
			UpperCamelCase__       =							old_name

			if "patch_embed" in old_name:
						UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							old_name.split("."	)

						if layer == "0":
									UpperCamelCase__       =							old_name.replace("0"					,  "convolution1"	)
						elif layer == "1":
									UpperCamelCase__       =							old_name.replace("1"					,  "batchnorm_before"	)
						elif layer == "3":
									UpperCamelCase__       =							old_name.replace("3"					,  "convolution2"	)
						else:
									UpperCamelCase__       =							old_name.replace("4"					,  "batchnorm_after"	)

			if "network" in old_name and re.search(r"\d\.\d"					,  _A	):
						UpperCamelCase__       =							r"\b\d{2}\b"
						if bool(re.search(_A					,  _A	)	):
									UpperCamelCase__       =							re.search(r"\d\.\d\d."					,  _A	).group()
						else:
									UpperCamelCase__       =							re.search(r"\d\.\d."					,  _A	).group()
						if int(match[0]	) < 6:
									UpperCamelCase__       =							old_name.replace(_A					,  ""	)
									UpperCamelCase__       =							trimmed_name.replace("network"					,  match[0] + ".meta4D_layers.blocks." + match[2:-1]	)
									UpperCamelCase__       =							"intermediate_stages." + trimmed_name
						else:
									UpperCamelCase__       =							old_name.replace(_A					,  ""	)
									if int(match[2]	) < num_meta4D_last_stage:
												UpperCamelCase__       =							trimmed_name.replace("network"					,  "meta4D_layers.blocks." + match[2]	)
									else:
												UpperCamelCase__       =							str(int(match[2]	) - num_meta4D_last_stage	)
												UpperCamelCase__       =							trimmed_name.replace("network"					,  "meta3D_layers.blocks." + layer_index	)
												if "norm1" in old_name:
															UpperCamelCase__       =							trimmed_name.replace("norm1"					,  "layernorm1"	)
												elif "norm2" in old_name:
															UpperCamelCase__       =							trimmed_name.replace("norm2"					,  "layernorm2"	)
												elif "fc1" in old_name:
															UpperCamelCase__       =							trimmed_name.replace("fc1"					,  "linear_in"	)
												elif "fc2" in old_name:
															UpperCamelCase__       =							trimmed_name.replace("fc2"					,  "linear_out"	)

									UpperCamelCase__       =							"last_stage." + trimmed_name

			elif "network" in old_name and re.search(r".\d."					,  _A	):
						UpperCamelCase__       =							old_name.replace("network"					,  "intermediate_stages"	)

			if "fc" in new_name:
						UpperCamelCase__       =							new_name.replace("fc"					,  "convolution"	)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						UpperCamelCase__       =							new_name.replace("norm1"					,  "batchnorm_before"	)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						UpperCamelCase__       =							new_name.replace("norm2"					,  "batchnorm_after"	)
			if "proj" in new_name:
						UpperCamelCase__       =							new_name.replace("proj"					,  "projection"	)
			if "dist_head" in new_name:
						UpperCamelCase__       =							new_name.replace("dist_head"					,  "distillation_classifier"	)
			elif "head" in new_name:
						UpperCamelCase__       =							new_name.replace("head"					,  "classifier"	)
			elif "patch_embed" in new_name:
						UpperCamelCase__       =							"efficientformer." + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						UpperCamelCase__       =							new_name.replace("norm"					,  "layernorm"	)
						UpperCamelCase__       =							"efficientformer." + new_name
			else:
						UpperCamelCase__       =							"efficientformer.encoder." + new_name

			return new_name
def 							UpperCamelCase_(					_A :int					,  _A :List[Any]	)->		Dict:
			for key in checkpoint.copy().keys():
						UpperCamelCase__       =							checkpoint.pop(_A	)
						UpperCamelCase__       =							val

			return checkpoint
def 							UpperCamelCase_(					)->		Optional[Any]:
			UpperCamelCase__       =							"http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase__       =							Image.open(requests.get(_A					,  stream=_A	).raw	)

			return image
def 							UpperCamelCase_(					_A :Path					,  _A :Path					,  _A :Path					,  _A :bool	)->		Union[str, Any]:
			UpperCamelCase__       =							torch.load(_A					,  map_location="cpu"	)["model"]
			UpperCamelCase__       =							EfficientFormerConfig.from_json_file(_A	)
			UpperCamelCase__       =							EfficientFormerForImageClassificationWithTeacher(_A	)
			UpperCamelCase__       =							"_".join(checkpoint_path.split("/"	)[-1].split("."	)[0].split("_"	)[:-1]	)

			UpperCamelCase__       =							config.depths[-1] - config.num_metaad_blocks + 1
			UpperCamelCase__       =							convert_torch_checkpoint(_A					,  _A	)

			model.load_state_dict(_A	)
			model.eval()

			UpperCamelCase__       =							{
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			# prepare image
			UpperCamelCase__       =							prepare_img()
			UpperCamelCase__       =							2_56
			UpperCamelCase__       =							2_24
			UpperCamelCase__       =							EfficientFormerImageProcessor(
			    size={"shortest_edge": image_size}					,  crop_size={"height": crop_size, "width": crop_size}					,  resample=pillow_resamplings["bicubic"]					,  )
			UpperCamelCase__       =							processor(images=_A					,  return_tensors="pt"	).pixel_values

			# original processing pipeline
			UpperCamelCase__       =							Compose(
			    [
			        Resize(_A					,  interpolation=pillow_resamplings["bicubic"]	),
			        CenterCrop(_A	),
			        ToTensor(),
			        Normalize(_A					,  _A	),
			    ]	)
			UpperCamelCase__       =							image_transforms(_A	).unsqueeze(0	)

			assert torch.allclose(_A					,  _A	)

			UpperCamelCase__       =							model(_A	)
			UpperCamelCase__       =							outputs.logits

			UpperCamelCase__       =							(1, 10_00)

			if "l1" in model_name:
						UpperCamelCase__       =							torch.Tensor(
						    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]	)
						assert torch.allclose(logits[0, :10]					,  _A					,  atol=1e-3	)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						UpperCamelCase__       =							torch.Tensor(
						    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]	)
						assert torch.allclose(logits[0, :10]					,  _A					,  atol=1e-3	)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						UpperCamelCase__       =							torch.Tensor(
						    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]	)
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'''	)

			# Save Checkpoints
			Path(_A	).mkdir(exist_ok=_A	)
			model.save_pretrained(_A	)
			print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''	)
			processor.save_pretrained(_A	)
			print(F'''Processor successfuly saved at {pytorch_dump_path}'''	)

			if push_to_hub:
						print("Pushing model to the hub..."	)

						model.push_to_hub(
						    repo_id=F'''Bearnardd/{pytorch_dump_path}'''					,  commit_message="Add model"					,  use_temp_dir=_A					,  )
						processor.push_to_hub(
						    repo_id=F'''Bearnardd/{pytorch_dump_path}'''					,  commit_message="Add image processor"					,  use_temp_dir=_A					,  )


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--pytorch_model_path',
						    default=None,
						    type=str,
						    required=True,
						    help='Path to EfficientFormer pytorch checkpoint.',
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The json file for EfficientFormer model config.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)

						parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
						parser.add_argument(
						    '--no-push_to_hub',
						    dest='push_to_hub',
						    action='store_false',
						    help='Do not push model and image processor to the hub',
						)
						parser.set_defaults(push_to_hub=True)

						__UpperCamelCase     		=							parser.parse_args()
						convert_efficientformer_checkpoint(
						    checkpoint_path=args.pytorch_model_path,
						    efficientformer_config_file=args.config_file,
						    pytorch_dump_path=args.pytorch_dump_path,
						    push_to_hub=args.push_to_hub,
						)






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()

						parser.add_argument(
						    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
						)
						# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
						parser.add_argument(
						    '--original_config_file',
						    default=None,
						    type=str,
						    help='The YAML config file corresponding to the original architecture.',
						)
						parser.add_argument(
						    '--num_in_channels',
						    default=None,
						    type=int,
						    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
						)
						parser.add_argument(
						    '--scheduler_type',
						    default='pndm',
						    type=str,
						    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
						)
						parser.add_argument(
						    '--pipeline_type',
						    default=None,
						    type=str,
						    help=(
						        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
						        '. If `None` pipeline will be automatically inferred.'
						    ),
						)
						parser.add_argument(
						    '--image_size',
						    default=None,
						    type=int,
						    help=(
						        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
						        ' Base. Use 768 for Stable Diffusion v2.'
						    ),
						)
						parser.add_argument(
						    '--prediction_type',
						    default=None,
						    type=str,
						    help=(
						        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
						        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
						    ),
						)
						parser.add_argument(
						    '--extract_ema',
						    action='store_true',
						    help=(
						        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
						        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
						        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
						    ),
						)
						parser.add_argument(
						    '--upcast_attention',
						    action='store_true',
						    help=(
						        'Whether the attention computation should always be upcasted. This is necessary when running stable'
						        ' diffusion 2.1.'
						    ),
						)
						parser.add_argument(
						    '--from_safetensors',
						    action='store_true',
						    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
						)
						parser.add_argument(
						    '--to_safetensors',
						    action='store_true',
						    help='Whether to store pipeline in safetensors format or not.',
						)
						parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
						parser.add_argument(
						    '--stable_unclip',
						    type=str,
						    default=None,
						    required=False,
						    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
						)
						parser.add_argument(
						    '--stable_unclip_prior',
						    type=str,
						    default=None,
						    required=False,
						    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
						)
						parser.add_argument(
						    '--clip_stats_path',
						    type=str,
						    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
						    required=False,
						)
						parser.add_argument(
						    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
						)
						parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
						parser.add_argument(
						    '--vae_path',
						    type=str,
						    default=None,
						    required=False,
						    help='Set to a path, hub id to an already converted vae to not convert it again.',
						)
						__UpperCamelCase     		=							parser.parse_args()

						__UpperCamelCase     		=							download_from_original_stable_diffusion_ckpt(
						    checkpoint_path=args.checkpoint_path,
						    original_config_file=args.original_config_file,
						    image_size=args.image_size,
						    prediction_type=args.prediction_type,
						    model_type=args.pipeline_type,
						    extract_ema=args.extract_ema,
						    scheduler_type=args.scheduler_type,
						    num_in_channels=args.num_in_channels,
						    upcast_attention=args.upcast_attention,
						    from_safetensors=args.from_safetensors,
						    device=args.device,
						    stable_unclip=args.stable_unclip,
						    stable_unclip_prior=args.stable_unclip_prior,
						    clip_stats_path=args.clip_stats_path,
						    controlnet=args.controlnet,
						    vae_path=args.vae_path,
						)

						if args.half:
												pipe.to(torch_dtype=torch.floataa)

						if args.controlnet:
												# only save the controlnet model
												pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
						else:
												pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


